For model training, we employ contrastive loss [1]} with cross-batch memory [2]}.
Our initial experiments showed this combination of methods outperforms other metric learning losses, such as triplet loss [3]}, [4]}, [5]} and AP loss [6]}.
By using the cross-batch memory, our model can train with more beneficial and diversified negative pairs.
